January 2022 arXiv papers — page 17

Andreas Kreisel, P. J. Hirschfeld, Brian M. Andersen

Recent resonant inelastic x-ray scattering (RIXS) experiments have detected a significant high-energy spin-excitation anisotropy in the nematic phase of the enigmatic iron-based superconductor FeSe, whose origin remains controversial. We apply an itinerant model previously used to describe the spin-excitation anisotropy as measured by neutron scattering meas

J. Kluson

We study string theory with global momentum living on de Sitter space. We also show that this presumption leads to the string with deformed dispersion relation.

Eleni Straitouri, Lequn Wang, Nastaran Okati, Manuel Gomez Rodriguez

Automated decision support systems promise to help human experts solve multiclass classification tasks more efficiently and accurately. However, existing systems typically require experts to understand when to cede agency to the system or when to exercise their own agency. Otherwise, the experts may be better off solving the classification tasks on their own

Zeyu Lu, Xingyu Gao, Haoyong Yu

The tactile sensing capabilities of human hands are essential in performing daily activities. Simultaneously perceiving normal and shear forces via the mechanoreceptors integrated into the hands enables humans to achieve daily tasks like grasping delicate objects. In this paper, we design and fabricate a novel biomimetic tactile sensor with skin-like heterog

Wen-Xiang Chen, Yao-Guang Zheng

This paper adds a new variable y($\mu^{\prime}=y(\omega+m N^{\phi})$) to extend the results of the classic paper. We exploit the properties of curve integrals. When y is greater than a certain limit, the effective potential of the equation has no pole, then there is no potential well outside the event horizon, when $\sqrt{2(m^2)}/{ r^2_+}< \omega < m\varOmeg

Federico Castelletti, Alessandro Mascaro

Directed Acyclic Graphs (DAGs) provide a powerful framework to model causal relationships among variables in multivariate settings; in addition, through the do-calculus theory, they allow for the identification and estimation of causal effects between variables also from pure observational data. In this setting, the process of inferring the DAG structure fro

Hans Triebel

This is the direct continuation of the paper "Mapping properties of Fourier transforms" (arXiv:2112.04896) using the same notation as there without further explanations. It deals with continuous and compact mappings of the Fourier transform $F$ between some weighted function spaces on $\mathbb{R}^n$.

Arpith Kumar, Anosh Joseph

Self-interacting scalar quantum field theories possessing $PT$-symmetry are physically admissible since their energy spectrum is real and bounded below. However, models with $PT$-invariant potentials can have complex actions in general and a non-perturbative study of such systems using methods based on traditional Monte Carlo is hindered due to numerical sig

Dhiraj Kumar Hazra, Akhil Antony, Arman Shafieloo

Acoustic peaks in the Cosmic Microwave Background (CMB) temperature spectrum as observed by the Planck satellite appear to be smoother than our expectation from the standard model lensing effect. This anomalous effect can be also mimicked by a spatially closed Universe with a very low value of Hubble constant that consequently aggravates the already existing

Shuang Wu, Zhenguang Li, Shijian Lu, Li Cheng

Music and dance have always co-existed as pillars of human activities, contributing immensely to the cultural, social, and entertainment functions in virtually all societies. Notwithstanding the gradual systematization of music and dance into two independent disciplines, their intimate connection is undeniable and one art-form often appears incomplete withou

Kuldeep Purohit, Srimanta Mandal, A. N. Rajagopalan

Recent advances in the design of convolutional neural network (CNN) have yielded significant improvements in the performance of image super-resolution (SR). The boost in performance can be attributed to the presence of residual or dense connections within the intermediate layers of these networks. The efficient combination of such connections can reduce the

Gianluca Passarelli, Ka-Wa Yip, Daniel A. Lidar, Procolo Lucignano

We study adiabatic reverse annealing (ARA) in an open system. In the closed system (unitary) setting, this annealing protocol allows avoidance of first-order quantum phase transitions of selected models, resulting in an exponential speedup compared with standard quantum annealing, provided that the initial state of the algorithm is close in Hamming distance

Kuldeep Purohit, Srimanta Mandal, A. N. Rajagopalan

Efficiency of gradient propagation in intermediate layers of convolutional neural networks is of key importance for super-resolution task. To this end, we propose a deep architecture for single image super-resolution (SISR), which is built using efficient convolutional units we refer to as mixed-dense connection blocks (MDCB). The design of MDCB combines the

He Sun, Mingkun Li, Chun-Guang Li

Unsupervised person re-identification (ReID) aims to match a query image of a pedestrian to the images in gallery set without supervision labels. The most popular approaches to tackle unsupervised person ReID are usually performing a clustering algorithm to yield pseudo labels at first and then exploit the pseudo labels to train a deep neural network. Howeve

Jie Zhang, Lei Zhang, Gang Li, Chao Wu

Adversarial examples are inputs for machine learning models that have been designed by attackers to cause the model to make mistakes. In this paper, we demonstrate that adversarial examples can also be utilized for good to improve the performance of imbalanced learning. We provide a new perspective on how to deal with imbalanced data: adjust the biased decis

Andrew C. Cullen, Tansu Alpcan, Alexander C. Kalloniatis

We apply computational Game Theory to a unification of physics-based models that represent decision-making across a number of agents within both cooperative and competitive processes. Here the competitors try to both positively influence their own returns, while negatively affecting those of their competitors. Modelling these interactions with the so-called

Yutong Wang, Guillaume Sartoretti

Decentralized cooperation in partially-observable multi-agent systems requires effective communications among agents. To support this effort, this work focuses on the class of problems where global communications are available but may be unreliable, thus precluding differentiable communication learning methods. We introduce FCMNet, a reinforcement learning b

Hannes Dänschel, Volker Mehrmann, Marius Roland, Martin Schmidt

We propose an adaptive optimization algorithm for operating district heating networks in a stationary regime. The behavior of hot water flow in the pipe network is modeled using the incompressible Euler equations and a suitably chosen energy equation. By applying different simplifications to these equations, we derive a catalog of models. Our algorithm is ba

Silouanos Brazitikos, Apostolos Giannopoulos, Minas Pafis

Given a probability measure $\mu $ on ${\mathbb R}^n$, Tukey's half-space depth is defined for any $x\in {\mathbb R}^n$ by $\varphi_{\mu }(x)=\inf\{\mu (H):H\in {\cal H}(x)\}$, where ${\cal H}(x)$ is the set of all half-spaces $H$ of ${\mathbb R}^n$ containing $x$. We show that if $\mu $ is log-concave then $$e^{-c_1n}\leq \int_{\mathbb{R}^n}\varphi_{\mu }(x

Weike Ye, Hui Zheng, Chi Chen, Shyue Ping Ong

The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk cohesive energy, can be described purely by four geometric features. By machine learning on a large computed database of 361 small $\Sigma$ ($\Sigma < 10$) GBs of more than 50 meta

Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley

Pretrained general-purpose language models can achieve state-of-the-art accuracies in various natural language processing domains by adapting to downstream tasks via zero-shot, few-shot and fine-tuning techniques. Because of their success, the size of these models has increased rapidly, requiring high-performance hardware, software, and algorithmic technique

Naoki Sato, Hideaki Iiduka

Previous results have shown that a two time-scale update rule (TTUR) using different learning rates, such as different constant rates or different decaying rates, is useful for training generative adversarial networks (GANs) in theory and in practice. Moreover, not only the learning rate but also the batch size is important for training GANs with TTURs and t

Antonio Greco, Francesca Gladiali

In this manuscript we consider semilinear PDEs, with a convex nonlinearity, in a sector-like domain. Using cylindrical coordinates $(r, \theta, z)$, we investigate the shape of solutions whose derivative in $\theta$ vanishes at the boundary. We prove that any solution with Morse index less than two must be either independent of $\theta$ or strictly monotone

Dun Wang, Kaixuan Guo, Yanying Zhu, Jia Sun

Biodegradable elastic scaffolds have attracted more and more attention in the field of soft tissue repair and tissue engineering. These scaffolds made of porous bioelastomers support tissue ingrowth along with their own degradation. It is necessary to develop a computer-aided analyzing method based on ultrasound images to identify the degradation performance

Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon

Federated learning (FL) is an emerging paradigm that permits a large number of clients with heterogeneous data to coordinate learning of a unified global model without the need to share data amongst each other. A major challenge in federated learning is the heterogeneity of data across client, which can degrade the performance of standard FL algorithms. Stan

Mokhtar Kirane, Ahmad Z. Fino, Bashir Ahmad

Some results on nonexistence of nontrivial solutions to some time and space fractional differential evolution equations with transformed space argument are obtained via the nonlinear capacity method. The analysis is then used for a $2\times 2$ system of equations with transformed space arguments.

Chen Li, Kevin Lewis

Robotic spacecraft have helped expand our reach for many planetary exploration missions. Most ground mobile planetary exploration robots use wheeled or modified wheeled platforms. Although extraordinarily successful at completing intended mission goals, because of the limitations of wheeled locomotion, they have been largely limited to benign, solid terrain

Håkan Carlsson, Isaac Skog, Gustaf Hendeby, Joakim Jaldén

We present a comprehensive framework for fusing measurements from multiple and generally placed accelerometers and gyroscopes to perform inertial navigation. Using the angular acceleration provided by the accelerometer array, we show that the numerical integration of the orientation can be done with second-order accuracy, which is more accurate compared to t

Luigi Cantini, Ali Zahra

We address the question of the large scale or hydrodynamic behavior of a 2-species generalization of TASEP (2-TASEP), consisting of two kinds of particles, moving in opposite directions and swapping their positions. We compute the rarefaction and shock solutions of the hydrodynamic equations of the model, showing that these equations form a Temple class syst

Chongkai Gao, Yizhou Jiang, Feng Chen

Hierarchical Imitation Learning (HIL) is an effective way for robots to learn sub-skills from long-horizon unsegmented demonstrations. However, the learned hierarchical structure lacks the mechanism to transfer across multi-tasks or to new tasks, which makes them have to learn from scratch when facing a new situation. Transferring and reorganizing modular su

Théo Ryffel, Francis Bach, David Pointcheval

We analyse the privacy leakage of noisy stochastic gradient descent by modeling R\'enyi divergence dynamics with Langevin diffusions. Inspired by recent work on non-stochastic algorithms, we derive similar desirable properties in the stochastic setting. In particular, we prove that the privacy loss converges exponentially fast for smooth and strongly convex

Dian-Teng Chen, Phillip Helms, Ashlyn R. Hale, Minseong Lee

The single-molecule magnet {Mn84} is a challenge to theory due to its high nuclearity. Building on our prior work which characterized the structure of the spectrum of this magnet, we directly compute two experimentally accessible observables, the field-dependent magnetization up to 75 T and the temperature-dependent heat capacity, using parameter free theory

Fatemeh Sheikh Shoaei, Alireza Nahvy, Zainalabedin Navabi

This paper presents a design for test (DFT)architecture for fast and scalable testing of array multipliers (MULTs). Regardless of the MULT size, our proposed testable architecture, without major changes in the original architecture, requires only five test vectors. Test pattern generation (TPG) is done by combining C-testability, bijectivity and deterministi

David Maltese, Chokri Ogabi

In this article, we deal with some problems involving a class of singularly perturbed elliptic operator. We prove the asymptotic preserving of a general Galerkin method associated to a semilinear problem. We use a particular Galerkin approximation to estimate the convergence rate on the whole domain, for the linear problem. Finally, we study the asymptotic b

Ce Yang, Weihao Gao, Di Wu, Chong Wang

Simulation of the dynamics of physical systems is essential to the development of both science and engineering. Recently there is an increasing interest in learning to simulate the dynamics of physical systems using neural networks. However, existing approaches fail to generalize to physical substances not in the training set, such as liquids with different

Yu Tian, Zhangkai Ni, Baoliang Chen, Shiqi Wang

Recent years have witnessed the dramatically increased interest in face generation with generative adversarial networks (GANs). A number of successful GAN algorithms have been developed to produce vivid face images towards different application scenarios. However, little work has been dedicated to automatic quality assessment of such GAN-generated face image

Loïc Foissy

We give here a review of results about double bialgebras, that is to say bialgebras with two coproducts, the first one being a comodule morphism for the coaction induced by the second one. An accent is put on the case of connected bialgebras. The subjects of these results are the monoid of characters and their actions, polynomial invariants, the antipode and

B. G. Kodge

The present study is an attempt made to create a new tool for the analysis of Land Use Land Cover (LUCL) images in 3D visualization. This study mainly uses spatial data mining techniques on high resolution LULC satellite imagery. Visualization of feature space allows exploration of patterns in the image data and insight into the classification process and re

Chengping Rao, Pu Ren, Yang Liu, Hao Sun

There have been growing interests in leveraging experimental measurements to discover the underlying partial differential equations (PDEs) that govern complex physical phenomena. Although past research attempts have achieved great success in data-driven PDE discovery, the robustness of the existing methods cannot be guaranteed when dealing with low-quality m

André Beauducel

Effects of performing R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. It was noted that estimating a model comprising R- and Q-factors has to face loading indeterminacy beyond rotational indeterminacy. Although R-factor analysis of data based on a population model comprising R- and Q-factors i

Songxiang Liu, Dan Su, Dong Yu

Denoising diffusion probabilistic models (DDPMs) are expressive generative models that have been used to solve a variety of speech synthesis problems. However, because of their high sampling costs, DDPMs are difficult to use in real-time speech processing applications. In this paper, we introduce DiffGAN-TTS, a novel DDPM-based text-to-speech (TTS) model ach

Ben Moonen

We show that the Coleman-Oort conjecture can be reduced to three particular cases. As an application we extend a result of Lu and Zuo, to the effect that for g at least 8 the Coleman-Oort conjecture is true on the hyperelliptic locus.

Yanfei Ye, Hanshuang Chen

In the present work, we study random walks on complex networks subject to stochastic resetting when the resetting probability is node-dependent. Using a renewal approach, we derive the exact expressions of the stationary occupation probabilities of the walker on each node and the mean first passage time between arbitrary two nodes. Finally, we demonstrate ou

Andrea Lucchini

Let $w \in F_2$ be a word and let $m$ and $n$ be two positive integers. We say that a finite group $G$ has the $w_{m,n}$-property if however a set $M$ of $m$ elements and a set $N$ of $n$ elements of the group is chosen, there exist at least one element of $x \in M$ and at least one element of $y \in M$ such that $w(x,y)=1.$ Assume that there exists a consta

Rui Wang, Robin Walters, Rose Yu

Incorporating symmetry as an inductive bias into neural network architecture has led to improvements in generalization, data efficiency, and physical consistency in dynamics modeling. Methods such as CNNs or equivariant neural networks use weight tying to enforce symmetries such as shift invariance or rotational equivariance. However, despite the fact that p

Thien Le, Stefanie Jegelka

The implicit bias induced by the training of neural networks has become a topic of rigorous study. In the limit of gradient flow and gradient descent with appropriate step size, it has been shown that when one trains a deep linear network with logistic or exponential loss on linearly separable data, the weights converge to rank-1 matrices. In this paper, we

Netai Bhukta, Souvik Manik, Sabyasachi Pal, Sushanta K. Mondal

Giant radio sources (GRSs) are the single largest astrophysical objects known in the universe that have grown to megaparsec scales ($\ge$ 0.7 Mpc). GRSs are much rarer compared with normal-sized radio galaxies. Still, the reason for the formation of their gigantic sizes is under debate. We systematically search for GRSs from the TIFR GMRT Sky Survey Alternat

Jin Young Shin, Jae Yong Lee, Hyung Ju Hwang

Learning the mapping between two function spaces has garnered considerable research attention. However, learning the solution operator of partial differential equations (PDEs) remains a challenge in scientific computing. Fourier neural operator (FNO) was recently proposed to learn solution operators, and it achieved an excellent performance. In this study, w

Anupriya Nyayban, Subhasis Panda, Avijit Chowdhury

Divalent cations mixed lead halide perovskites with enhanced performances, high stabilities, and reduced toxicity are requisite to make persistent progress in perovskite solar cells. However, the mixing strategy is not reported extensively in search of a lead reduced structure. Herein, we report the structural, electronic and optical properties of RbPb{1-x}M

Yuhao Ding, Javad Lavaei

We consider primal-dual-based reinforcement learning (RL) in episodic constrained Markov decision processes (CMDPs) with non-stationary objectives and constraints, which plays a central role in ensuring the safety of RL in time-varying environments. In this problem, the reward/utility functions and the state transition functions are both allowed to vary arbi

Himanshi Charotia, Abhishek Garg, Gaurav Dhama, Naman Maheshwari

Planning based on long and short term time series forecasts is a common practice across many industries. In this context, temporal aggregation and reconciliation techniques have been useful in improving forecasts, reducing model uncertainty, and providing a coherent forecast across different time horizons. However, an underlying assumption spanning all these

Changwei Xu, Jianfei Yang, Haoran Tang, Han Zou

Unsupervised Domain Adaptation (UDA), a branch of transfer learning where labels for target samples are unavailable, has been widely researched and developed in recent years with the help of adversarially trained models. Although existing UDA algorithms are able to guide neural networks to extract transferable and discriminative features, classifiers are mer

Seungki Min, Ciamac C. Moallemi, Costis Maglaras

We consider a liquidation problem in which a risk-averse trader tries to liquidate a fixed quantity of an asset in the presence of market impact and random price fluctuations. The trader encounters a trade-off between the transaction costs incurred due to market impact and the volatility risk of holding the position. Our formulation begins with a continuous-

H. Bakke, M. Carlsson, L. Rouppe van der Voort, B. V. Gudiksen

Heating signatures from small-scale magnetic reconnection events in the solar atmosphere have proven to be difficult to detect through observations. Numerical models that reproduce flaring conditions are essential in the understanding of how nanoflares may act as a heating mechanism of the corona. We study the effects of non-thermal electrons in synthetic sp

Masahito Hayashi

Bennet-Brassard 1984 (BB84) protocol, we optimize the ratio of the choice of two bases, the bit basis and the phase basis by using the second order expansion for the length of the generation keys under the coherent attack. This optimization addresses the trade-off between the loss of transmitted bits due to the disagreement of their bases and the estimation

Youting Liang, Junxia Zhou, Rongbo Wu, Zhiwei Fang

We overcome the difficulty in realizing a monolithic waveguide-coupled microring laser integrated on erbium-doped thin film lithium niobate (Er: TFLN) using photolithography assisted chemo-mechanical etching (PLACE) technique. We demonstrate an integrated single-frequency microring laser operating around 1531 nm wavelength. The PLACE technique, enabling inte

Martin Knor, Riste Skrekovski

This paper is devoted to Wiener index of directed graphs, more precisely of directed grids. The grid $G_{m,n}$ is the Cartesian product $P_m\Box P_n$ of paths on $m$ and $n$ vertices, and in a particular case when $m=2$, it is a called the ladder graph $L_n$. Kraner \v{S}umenjak et al. proved that the maximum Wiener index of a digraph, which is obtained by o

Lalithkumar Seenivasan, Sai Mitheran, Mobarakol Islam, Hongliang Ren

Global and local relational reasoning enable scene understanding models to perform human-like scene analysis and understanding. Scene understanding enables better semantic segmentation and object-to-object interaction detection. In the medical domain, a robust surgical scene understanding model allows the automation of surgical skill evaluation, real-time mo

Valts Krumins, Artis Kruzins, Maris Tamanis, Ruvin Ferber

The oscillation continuum in laser-induced fluorescence spectra of bound-free $c^3\Sigma^+ \to a^3\Sigma^+$ and (4)$^1\Sigma^+ \to X^1\Sigma^+$ transitions of the KCs molecule were recorded by Fourier-transform spectrometer and modelled under the adiabatic approximation. The required interatomic potentials for ground $a^3\Sigma^+$ and $X^1\Sigma^+$ states we

Kaito Kimura

In this paper, we consider the openness of the P-locus of a finitely generated module over a commutative noetherian ring in the case where P is each of the properties FID, Gor, CM, MCM, (S_n), and (T_n). One of the main results asserts that FID-loci over an acceptable ring are open. We give a module version of the Nagata criterion, and prove that it holds fo

Francois G. Meyer

We address the following foundational question: what is the population, and sample, Frechet mean (or median) graph of an ensemble of inhomogeneous Erdos-Renyi random graphs? We prove that if we use the Hamming distance to compute distances between graphs, then the Frechet mean (or median) graph of an ensemble of inhomogeneous random graphs is obtained by thr

Xi-Yu Luo, Yong Yu, Jian-Long Liu, Ming-Yang Zheng

Quantum internet gives the promise of getting all quantum resources connected, and it will enable applications far beyond a localized scenario. A prototype is a network of quantum memories that are entangled and well separated. Previous realizations are limited in the distance. In this paper, we report the establishment of remote entanglement between two ato

Sina Taheri, Vassilis Kekatos, Harsha Veeramachaneni, Baosen Zhang

Bundling a large number of distributed energy resources through a load aggregator has been advocated as an effective means to integrate such resources into whole-sale energy markets. To ease market clearing, system operators allow aggregators to submit bidding models of simple prespecified polytopic shapes. Aggregators need to carefully design and commit to

Bahman Moghimi, Camille Kamga, Abolfazl Safikhani, Sandeep Mudigonda

The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets d

Kyeong-Joong Jeong, Yong-Min Shin

Detecting anomalies in multivariate time-series data is essential in many real-world applications. Recently, various deep learning-based approaches have shown considerable improvements in time-series anomaly detection. However, existing methods still have several limitations, such as long training time due to their complex model designs or costly tuning proc

Jiawang Nie, Li Wang, Zequn Zheng

Multi-view learning is frequently used in data science. The pairwise correlation maximization is a classical approach for exploring the consensus of multiple views. Since the pairwise correlation is inherent for two views, the extensions to more views can be diversified and the intrinsic interconnections among views are generally lost. To address this issue,

Ting Ye, Jun Shao, Yanyao Yi

Nonparametric covariate adjustment is considered for log-rank type tests of treatment effect with right-censored time-to-event data from clinical trials applying covariate-adaptive randomization. Our proposed covariate-adjusted log-rank test has a simple explicit formula and a guaranteed efficiency gain over the unadjusted test. We also show that our propose

Siva Athreya, Nitya Gadhiwala, Ritvik R. Radhakrishnan

We prove the scale invariant Elliptic Harnack Inequality (EHI) for non-negative harmonic functions on ${\mathbb{Z}}^d$. The purpose of this note is to provide a simplified self-contained probabilistic proof of EHI in ${\mathbb{Z}}^d$ that is accessible at the undergraduate level. We use the Local Central Limit Theorem for simple symmetric random walks on ${\

Claus Bauer

We investigate the approximation to the number of primes in arithmetic progressions given by Vaughan. Instead of averaging the expected error term over all residue classes to modules in a given range, here we only consider subsets of arithmetic progressions that satisfy additional congruence conditions and provide asymptotic approximations.

Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu

Automatic Audio Captioning (AAC) refers to the task of translating audio into a natural language that describes the audio events, source of the events and their relationships. The limited samples in AAC datasets at present, has set up a trend to incorporate transfer learning with Audio Event Detection (AED) as a parent task. Towards this direction, in this p

Lingxiao Li, Noam Aigerman, Vladimir G. Kim, Jiajin Li

Finding multiple solutions of non-convex optimization problems is a ubiquitous yet challenging task. Most past algorithms either apply single-solution optimization methods from multiple random initial guesses or search in the vicinity of found solutions using ad hoc heuristics. We present an end-to-end method to learn the proximal operator of a family of tra

Bruno Hexsel, Heethesh Vhavle, Yi Chen

In this paper, we present a novel algorithm for point cloud registration for range sensors capable of measuring per-return instantaneous radial velocity: Doppler ICP. Existing variants of ICP that solely rely on geometry or other features generally fail to estimate the motion of the sensor correctly in scenarios that have non-distinctive features and/or repe

Jennifer Fowlie, Marios Hadjimichael, Maria M. Martins, Danfeng Li

The discovery of superconductivity in Nd$_{0.8}$Sr$_{0.2}$NiO$_2$ [1] introduced a new family of layered nickelate superconductors that has now been extended to include a range of Sr-doping [2, 3], Pr or La in place of Nd [4-6], and the 5-layer Nd$_6$Ni$_5$O$_{12}$ [7]. A number of studies indicate that electron correlations are strong in these materials [8-

Clifford V. Johnson

It is proposed that a complete understanding of two-dimensional quantum gravity and its emergence in random matrix models requires fully embracing {\it both} Wigner (statistics) and 't Hooft (geometry). Using non-perturbative definitions of random matrix models that yield various JT gravity and JT supergravity models on Euclidean surfaces of arbitrary topolo

Oliver M. Cliff, Annie G. Bryant, Joseph T. Lizier, Naotsugu Tsuchiya

Scientists have developed hundreds of techniques to measure the interactions between pairs of processes in complex systems. But these computational methods, from correlation coefficients to causal inference, rely on distinct quantitative theories that remain largely disconnected. Here we introduce a library of 237 statistics of pairwise interactions and asse

Paul Zhang, Dmitriy Smirnov, Justin Solomon

Much of computer-generated animation is created by manipulating meshes with rigs. While this approach works well for animating articulated objects like animals, it has limited flexibility for animating less structured free-form objects. We introduce Wassersplines, a novel trajectory inference method for animating unstructured densities based on recent advanc

James Wang, Cheng-Lin Yang

Generalization of deep neural networks remains one of the main open problems in machine learning. Previous theoretical works focused on deriving tight bounds of model complexity, while empirical works revealed that neural networks exhibit double descent with respect to both training sample counts and the neural network size. In this paper, we empirically exa

Yihao Lin, Ji Feng

Antiferromagnetic topological insulators harbor topological in-gap surface states protected by an anti-unitary $S$ symmetry, which is broken by the inevitable presence of domain walls. Whether an antiferromagnetic topological insulator with domain walls is gapless and metallic on its topological surfaces remains to be elucidated. We show that a single non-st

Wei Xue, Xiaojiang Peng

Stereo matching is crucial for binocular stereo vision. Existing methods mainly focus on simple disparity map fusion to improve stereo matching, which require multiple dense or sparse disparity maps. In this paper, we propose a simple yet novel scheme, termed feature disparity propagation, to improve general stereo matching based on matching cost volume and

Shiwen Zhao, Charles Crissman, Guillermo R Sapiro

Collaborative filtering is the de facto standard for analyzing users' activities and building recommendation systems for items. In this work we develop Sliced Anti-symmetric Decomposition (SAD), a new model for collaborative filtering based on implicit feedback. In contrast to traditional techniques where a latent representation of users (user vectors) and i

Addison Wood, Jory Schossau, Nick Sabaj, Richard Liu

A profound challenge for A-Life is to construct agents whose behavior is 'life-like' in a deep way. We propose an architecture and approach to constructing networks driving artificial agents, using processes analogous to the processes that construct and sculpt the brains of animals. Furthermore the instantiation of action is dynamic: the whole network respon

Anisha Banerjee, Andreas Lenz, Antonia Wachter-Zeh

Sequential decoding, commonly applied to substitution channels, is a sub-optimal alternative to Viterbi decoding with significantly reduced memory costs. In this work, a sequential decoder for convolutional codes over channels that are prone to insertion, deletion, and substitution errors, is described and analyzed. Our decoder expands the code trellis by a

Jieren Deng, Chenghong Wang, Xianrui Meng, Yijue Wang

In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks. Existing solutions either involve a trusted aggregator or require heavyweight cryptographic primitives, which degrades performance significantly. Moreover, many existing secure FL designs work only under the restrictive assumption that none of the clie

Ian W. Stephens, Philip C. Myers, Catherine Zucker, James M. Jackson

Star formation primarily occurs in filaments where magnetic fields are expected to be dynamically important. The largest and densest filaments trace spiral structure within galaxies. Over a dozen of these dense ($\sim$10$^4$\,cm$^{-3}$) and long ($>$10\,pc) filaments have been found within the Milky Way, and they are often referred to as "bones." Until now,

Shiyu Wang, Xiaojie Guo, Liang Zhao

Periodic graphs are graphs consisting of repetitive local structures, such as crystal nets and polygon mesh. Their generative modeling has great potential in real-world applications such as material design and graphics synthesis. Classical models either rely on domain-specific predefined generation principles (e.g., in crystal net design), or follow geometry

Yan Shuo Tan, Chandan Singh, Keyan Nasseri, Abhineet Agarwal

Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure. To overcome this bias, we propose

Nicolas Lanchier, Stephanie Reed

This paper studies an interacting particle system of interest in econophysics inspired from a model introduced in the physics literature. The original model consists of the customers of a single bank characterized by their capital, and the discrete-time dynamics consists of monetary transactions in which a random individual $x$ gives one coin to another rand

Yu-Hong Cai, Xiao-Jun Wu, Zhe Chen

Linear regression is a supervised method that has been widely used in classification tasks. In order to apply linear regression to classification tasks, a technique for relaxing regression targets was proposed. However, methods based on this technique ignore the pressure on a single transformation matrix due to the complex information contained in the data.

Meghal Gupta, Rachel Zhang

An interactive error correcting code ($\mathsf{iECC}$) is an interactive protocol with the guarantee that the receiver can correctly determine the sender's message, even in the presence of noise. This generalizes the concept of an error correcting code ($\mathsf{ECC}$), which is a non-interactive $\mathsf{iECC}$ that is known to have erasure resilience cappe

Hua Chen, Zejun Hong, Shunpeng Yang, Patrick M. Wensing

This paper studies capturability and push recovery for quadrupedal locomotion. Despite the rich literature on capturability analysis and push recovery control for legged robots, existing tools are developed mainly for bipeds or humanoids. Distinct quadrupedal features such as point contacts and multiple swinging legs prevent direct application of these metho

Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu

Safe reinforcement learning (RL) aims to learn policies that satisfy certain constraints before deploying them to safety-critical applications. Previous primal-dual style approaches suffer from instability issues and lack optimality guarantees. This paper overcomes the issues from the perspective of probabilistic inference. We introduce a novel Expectation-M

Mohamed Tarek, Yijiang Huang

Non-convex optimization problems have multiple local optimal solutions. Non-convex optimization problems are commonly found in numerous applications. One of the methods recently proposed to efficiently explore multiple local optimal solutions without random re-initialization relies on the concept of deflation. In this paper, different ways to use deflation i

Sergio Salinas, Nancy Hitschfeld-Kahler, Alejandro Ortiz-Bernardin, Hang Si

This paper presents an algorithm to generate a new kind of polygonal mesh obtained from triangulations. Each polygon is built from a terminal-edge region surrounded by edges that are not the longest-edge of any of the two triangles that share them. The algorithm is termed Polylla and is divided into three phases. The first phase consists of labeling each edg

Xiaoshuai Zhang, Rui Chen, Ang Li, Fanbo Xiang

In this paper, we focus on the simulation of active stereovision depth sensors, which are popular in both academic and industry communities. Inspired by the underlying mechanism of the sensors, we designed a fully physics-grounded simulation pipeline that includes material acquisition, ray-tracing-based infrared (IR) image rendering, IR noise simulation, and

Cheng-Qian Xu, D. L. Zhou

Anyons have been extensively investigated as information carriers in topological quantum computation. However, how to characterize the information flow in quantum networks composed of anyons is less understood, which motivates us to study quantum communication protocols in anyonic systems. Here we propose a general topologically protected protocol for quantu

Siheng Chen, Fabio Giardina, Gary P. T. Choi, L. Mahadevan

Geometric graph models of systems as diverse as proteins, robots, and mechanical structures from DNA assemblies to architected materials point towards a unified way to represent and control them in space and time. While much work has been done in the context of characterizing the behavior of these networks close to critical points associated with bond and ri

Fei Fei, Yuan Hu, Patrick Jenny

Simulating hypersonic flow around a space vehicle is challenging because of the multiscale and nonequilibrium nature inherent in these flows. To effectively deal with such flows, a novel particle particle hybrid scheme combining the stochastic particle Bhatnagar-Gross-Krook (BGK) method with Direct Simulation Monte Carlo (DSMC) was developed recently, but on

Marie Albenque, Laurent Ménard

We investigate the geometry of a typical spin cluster in random triangulations sampled with a probability proportional to the energy of an Ising configuration on their vertices, both in the finite and infinite volume settings. This model is known to undergo a combinatorial phase transition at an explicit critical temperature, for which its partition function

Jiatai Huang, Yan Dai, Longbo Huang

In this paper, we generalize the concept of heavy-tailed multi-armed bandits to adversarial environments, and develop robust best-of-both-worlds algorithms for heavy-tailed multi-armed bandits (MAB), where losses have $\alpha$-th ($1<\alpha\le 2$) moments bounded by $\sigma^\alpha$, while the variances may not exist. Specifically, we design an algorithm \tex

Laurent Ménard

We derive three critical exponents for Bernoulli site percolation on the on the Uniform Infinite Planar Triangulation (UIPT). First we compute explicitly the probability that the root cluster is infinite. As a consequence, we show that the off-critical exponent for site percolation on the UIPT is $\beta = 1/2$. Then we establish an integral formula for the g